Hidden Markov Model, HMM, speech recognition systems determine which previously trained, stored model best matches the string of input feature observations characterizing a given input speech utterance.
Prior art HMM speech recognition systems choose a model based on a best state sequence, in the maximum likelihood sense, at a specified time. Noise or inadequate training can cause a maximum likelihood state sequence associated with a model other than the correct model to be chosen.
Accordingly, there is a need for a method, apparatus, and radio for Hidden Markov Model speech recognition that optimizes the model selection especially in the presence of noise or inadequate training.